Normalizing flows (NFs) provide exact likelihoods and deterministic invertible sampling, but have historically lagged behind diffusion models for large-scale image generation. We identify a key obstacle: NFs are required to learn a single invertible transport over the full ambient space, making them highly sensitive to high-dimensional representations. This leads to a semantic-capacity mismatch in modern visual representation spaces, where semantic information is compact but encoded in overcomplete features. We propose SRC-Flow, which introduces a Semantic Representation Compressor (SRC) to compact high-dimensional RAE features into a low-dimensional semantic space before flow modeling and preserve reconstruction through the frozen RAE decoder. This compact space reduces the modeling burden of NFs and enables effective likelihood-based generation in semantic representation space. We further adopt constant noise regularization tailored to the fixed unconditional bijection learned by flows. On ImageNet 256x256 and 512x512, SRC-Flow achieves state-of-the-art generation quality among normalizing flow methods, with gFID scores of 1.65 and 2.07 under classifier-free guidance, while retaining exact likelihood computation in the compact semantic representation space and deterministic invertible sampling at the flow level. Codes and models will be released in the future.
Cheng-Han Chiang, Xiaofei Wang Microsoft, Linjie Li University of Washington and Microsoft, Chung-Ching Lin, Kevin Lin Microsoft, Shujie Liu, Zhendong Wang Microsoft, Zhengyuan Yang Microsoft 等 10 位作者
Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from interacting with the user during the user’s turn and can lead to high response latency when the model is thinking. To address this issue, we draw inspiration from the “think while listening” behavior of humans. In this paper, we propose SHANKS, a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to user input. SHANKS streams input speech in fixed-duration chunks and, as soon as a chunk is received, generates unspoken reasoning based on all previous speech and reasoning, while the user continues speaking. SHANKS uses unspoken reasoning to determine whether to interrupt the user and make tool calls to complete the task. We demonstrate that SHANKS enhances real-time user–SLM interaction in two scenarios: (1) SHANKS can listen to the user’s speech and interrupt when the user makes a mistake. (2) In a tool-augmented dialogue scenario, SHANKS can complete 56.9% of the tool calls before the user ends their turn. Overall, SHANKS is a step toward models that keep thinking throughout the conversation, not only after a turn ends. Demos can be found on the project page: https://d223302.github.io/SHANKS/.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表